#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
math functions
"""

from __future__ import print_function

from paddle.common_ops_import import *
from ..fluid.framework import core
from ..fluid.layers.layer_function_generator import _generate_doc_string_

# TODO: define math functions
# yapf: disable
__all__ = [
#            'abs',
#            'acos',
#            'asin',
           'atan',
#            'ceil',
#            'cos',
#            'cumsum',
#            'elementwise_add',
#            'elementwise_div',
#            'elementwise_floordiv',
#            'elementwise_max',
#            'elementwise_min',
#            'elementwise_mod',
#            'elementwise_mul',
#            'elementwise_pow',
#            'elementwise_sub',
#            'exp',
#            'floor',
#            'increment',
#            'log',
           'mul',
#            'multiplex',
           'pow',
#            'reciprocal',
#            'reduce_max',
#            'reduce_min',
#            'reduce_prod',
#            'reduce_sum',
#            'round',
#            'rsqrt',
#            'scale',
#            'sign',
           'sin',
           'sqrt',
#            'square',
#            'stanh',
           'sum',
#            'sums',
           'tanh',
#            'elementwise_sum',
#            'max',
#            'min',
#            'mm',
           'div',
           'add',
#            'atan',
#            'logsumexp',
#            'inverse',
#            'log1p',
#            'erf',
#            'addcmul',
#            'addmm']
]
# yapf: enable.


def generate_op_noattr(op_type):
    """Register the Python layer for an Operator without Attribute..

    Args:
       op_type: The name of the operator to be created.

    This function takes in the operator type (sin, tanh etc) and
    creates the operator functionality.

    """
    op_proto = OpProtoHolder.instance().get_op_proto(op_type)

    def func(x, name=None, out=None):
        if in_dygraph_mode():
            op = getattr(core.ops, op_type)
            return op(x)

        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                                 op_type)
        helper = LayerHelper(op_type, **locals())

        if name and out:
            warnings.warn(
                "Both name and out parameters have been set in fluid.tensor.math.%s(), only out will take effect to specify the result storage. "
                "You can discard either one to solve this warning." % op_type,
                category=UserWarning,
                stacklevel=2)
        if not out:
            out = helper.create_variable_for_type_inference(dtype=x.dtype)
        helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": out})
        return out

    func.__name__ = op_type
    func.__doc__ = _generate_doc_string_(
        op_proto,
        additional_args_lines=[
            "name(str, optional): The default value is None.  Normally there is no need for user to set this property.  For more information, please refer to :ref:`api_guide_Name`.\n    "
            "out(Variable, optional): The default value is None. Optional output can be any created Variable that meets the requirements to store the result of operation. if out is None, a new Varibale will be create to store the result."
        ])
    func.__doc__ = func.__doc__ + """

Return type
  Variable
Examples:
    .. code-block:: python

        import numpy as np
        
        import paddle
        import paddle.fluid as fluid

        inputs = fluid.data(name="x", shape = [None, 4], dtype='float32')
        output = paddle.%s(inputs)

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())

        #input.shape=1X4, batch_size=1
        img = np.array([[1.0, 2.0, 3.0, 4.0]]).astype(np.float32)
        res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output])
        print(res)
""" % op_type
    return func

@templatedoc()
def pow(input, exponent, out=None, name=None):
    """
    This is Pow Activation Operator.

    :math:`out = input^{exponent}`

    Args:
        input(Variable): A ``Tensor`` or ``LoDTensor`` . The data type is ``float32`` or ``float64``.
        exponent(float32|Variable): A scalar with type ``float32`` or a ``Tensor`` with shape [1] and type ``float32``.
        out (Variable, optional):  The Variable that stores results of the operation. 
            If out is None, a new Variable will be created to store the results.
        name(str, optional): The default value is None. Normally there is no need for user to set this property. 
            For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        Variable: A ``Tensor`` or ``LoDTensor``. The data type is same as ``input``.

    Examples:

        .. code-block:: python

            import paddle

            x = paddle.fluid.data(name="x", shape=[32,32], dtype="float32")

            # example 1: argument exponent is float
            res = paddle.fluid.data(name="output", shape=[32,32], dtype="float32")
            y_1 = paddle.pow(x, 2.0, out=res)
            # y_1 is x^{2.0}

            # example 2: argument exponent is Variable
            exponet_tensor = fluid.layers.fill_constant([1], "float32", 3.0)
            res = paddle.fluid.data(name="output", shape=[32,32], dtype="float32")
            y_2 = paddle.pow(x, exponent_tensor, out=res)
            # y_2 is x^{3.0}
    """
    helper = LayerHelper('pow', **locals())
    inputs = {'X': input}
    attrs = {}
    if isinstance(exponent, Variable):
        exponent.stop_gradient = True
        inputs['FactorTensor'] = exponent
    else:
        attrs['factor'] = exponent

    if out is None:
        out = helper.create_variable_for_type_inference(dtype=input.dtype)
    else:
        check_dtype(
            out.dtype, out.name,
            convert_dtype(input.dtype), 'pow',
            '(The out data type in pow must be the same with input data type.)')
        if name:
            warnings.warn(
                "The output Variable name of the paddle.tensor.pow operation can only be given by parameter out or name. \
                When parameter out and name are set at the same time, out has a higher priority than name. \
                Finally, the output Variable name is same as the out name %s"
                                                                              %
                out.name,
                category=UserWarning,
                stacklevel=2)

    helper.append_op(
        type='pow', inputs=inputs, outputs={'Out': out}, attrs=attrs)
    return out


def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, out=None, name=None):
    """
    Mul Operator.
    This operator is used to perform matrix multiplication for input $x$ and $y$.
    The equation is:

    ..  math::
        Out = x * y

    Both the input $x$ and $y$ can carry the LoD (Level of Details) information, or not. 
    But the output only shares the LoD information with input $x$.

    Args:
        x (Variable): The first input Tensor/LoDTensor of mul_op.
        y (Variable): The second input Tensor/LoDTensor of mul_op.
        x_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. 
            If the input $x$ is a tensor with more than two dimensions, $x$ will be flattened into a two-dimensional 
            matrix first. The flattening rule is: the first `num_col_dims` will be flattened to form the first 
            dimension of the final matrix (the height of the matrix), and the rest `rank(x) - num_col_dims` 
            dimensions are flattened to form the second dimension of the final matrix (the width of the matrix). 
            As a result, height of the flattened matrix is equal to the product of $x$'s first `x_num_col_dims` dimensions' 
            sizes, and width of the flattened matrix is equal to the product of $x$'s last `rank(x) - num_col_dims` 
            dimensions' size. For example, suppose $x$ is a 6-dimensional tensor with the shape [2, 3, 4, 5, 6], 
            and `x_num_col_dims` = 3. Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default is 1. 
        y_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the 
            input $y$ is a tensor with more than two dimensions, $y$ will be flattened into a two-dimensional matrix first. 
            The attribute `y_num_col_dims` determines how $y$ is flattened. See comments of `x_num_col_dims` for more details. 
            Default is 1. 
        out(Variable, optinal): The Variable that stores results of the operation. If out is None, 
            a new Variable will be created to store the results.
        name (str, optional): Name of the output. Normally there is no need for user to set this property. 
            For more information, please refer to :ref:`api_guide_Name`. Default is None. If both of out and name are not None, 
            the output name will be same as out. 

    Returns:
        Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of mul op.

    Examples:
        ..  code-block:: python
            
            import paddle
            dataX = paddle.fluid.data(name="dataX", append_batch_size = False, shape=[2, 5], dtype="float32")
            dataY = paddle.fluid.data(name="dataY", append_batch_size = False, shape=[5, 3], dtype="float32")
            
            res = paddle.fluid.data(name="output", append_batch_size = False, shape=[2, 3], dtype="float32")
            output = paddle.mul(dataX, dataY,
                                      x_num_col_dims = 1,
                                      y_num_col_dims = 1, 
                                      out=res)
            

    """
    inputs = {"X": [x], "Y": [y]}
    attrs = {"x_num_col_dims": x_num_col_dims, "y_num_col_dims": y_num_col_dims}
    if in_dygraph_mode():
        outs = core.ops.mul(inputs, attrs)
        return outs['Out'][0]

    helper = LayerHelper("mul", **locals())
    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'mul')
    check_variable_and_dtype(y, 'y', ['float16', 'float32', 'float64'], 'mul')

    if out is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        check_dtype(
            out.dtype, out.name,
            convert_dtype(x.dtype), 'mul',
            '(The out data type in pow must be the same with input data type.)')
        if name:
            warnings.warn(
                "The output Variable name of the paddle.tensor.pow operation can only be given by parameter out or name.\
                When parameter out and name are set at the same time, out has a higher priority than name. \
                Finally, the output Variable name is same as the out name %s"
                                                                              %
                out.name,
                category=UserWarning,
                stacklevel=2)
    helper.append_op(
        type="mul", inputs={"X": x,
                            "Y": y}, attrs=attrs, outputs={"Out": out})
    return out


__ops__noattr__ = [
    'atan',
    'sin',
    'sqrt',
    'tanh',
]

for _OP in set(__ops__noattr__):
    globals()[_OP] = generate_op_noattr(_OP)


@dygraph_only
def _elementwise_op_in_dygraph(x,
                               y,
                               axis=-1,
                               act=None,
                               use_mkldnn=False,
                               op_name=None):
    op = getattr(core.ops, op_name)
    out = op(x, y, 'axis', axis, 'use_mkldnn', use_mkldnn)

    return dygraph_utils._append_activation_in_dygraph(
        out, act, use_mkldnn=use_mkldnn)


def _elementwise_op(helper):
    op_type = helper.layer_type
    original_op_type = helper.kwargs.get('original_op_type', op_type)
    x = helper.kwargs.get('x', None)
    y = helper.kwargs.get('y', None)

    assert x is not None, 'x cannot be None in {}'.format(original_op_type)
    assert y is not None, 'y cannot be None in {}'.format(original_op_type)
    check_variable_and_dtype(
        x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'],
        original_op_type)
    check_variable_and_dtype(
        y, 'y', ['float16', 'float32', 'float64', 'int32', 'int64'],
        original_op_type)

    axis = helper.kwargs.get('axis', -1)
    use_mkldnn = helper.kwargs.get('use_mkldnn', False)
    name = helper.kwargs.get('name', None)
    out = helper.kwargs.get('out', None)
    if out is None:
        if name is None:
            out = helper.create_variable_for_type_inference(dtype=x.dtype)
        else:
            out = helper.create_variable(
                name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type=op_type,
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out},
        attrs={'axis': axis,
               'use_mkldnn': use_mkldnn})
    return helper.append_activation(out)


def add(x, y, alpha=1, out=None, name=None):
    """
Examples:

    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.array([2, 3, 4]).astype('float32'),
                "y": np.array([1, 5, 2]).astype('float32')
            }

        x = fluid.data(name="x", shape=[3], dtype='float32')
        y = fluid.data(name="y", shape=[3], dtype='float32')
        z1 = paddle.add(x, y)
        z2 = paddle.add(x, y, alpha=10)
        # z = x + y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z1.name, z2.name])

        print(z_value[0]) # [3., 8., 6.]
        print(z_value[1]) # [12. 53. 24.]


    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.ones((2, 3, 4, 5)).astype('float32'),
                "y": np.zeros((4, 5)).astype('float32')
            }

        x = fluid.data(name="x", shape=[2, 3, 4, 5], dtype='float32')
        y = fluid.data(name="y", shape=[4, 5], dtype='float32')
        z = paddle.add(x, y, name='z')
        # z = x + y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])

        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
                "y": np.random.randint(1, 5, size=[5]).astype('float32')
            }

        x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
        y = fluid.data(name="y", shape=[5], dtype='float32')
        z = paddle.add(x, y)
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])
        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        x = fluid.data(name="x", shape=[3], dtype="float32")
        y = fluid.data(name='y', shape=[3], dtype='float32')

        output = fluid.data(name="output", shape=[3], dtype="float32")
        z = paddle.add(x, y, out=output)

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        data1 = np.array([2, 3, 4], dtype='float32')
        data2 = np.array([1, 5, 2], dtype='float32')
        z_value = exe.run(feed={'x': data1,
                                'y': data2},
                                fetch_list=[z])
        print(z_value[0]) # [3. 8. 6.]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        with fluid.dygraph.guard():
            np_x = np.array([2, 3, 4]).astype('float64')
            np_y = np.array([1, 5, 2]).astype('float64')
            x = fluid.dygraph.to_variable(np_x)
            y = fluid.dygraph.to_variable(np_y)
            z = paddle.add(x, y, alpha=-0.5)
            np_z = z.numpy()
            print(np_z)  # [1.5, 0.5, 3. ]

    """
    op_type = 'elementwise_add'
    axis = -1
    act = None
    if alpha != 1:
        y = scale(y, scale=alpha)
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)

    original_op_type = 'add'
    if name and out:
        warnings.warn(
            "Both name and out parameters have been set in paddle.tensor.%s, only out will take effect to specify the result storage. "
            "You can discard either one to solve this warning." %
            original_op_type,
            category=UserWarning,
            stacklevel=2)
    return _elementwise_op(LayerHelper(op_type, **locals()))


def div(x, y, out=None, name=None):
    """
Examples:

    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.array([2, 3, 4]).astype('float32'),
                "y": np.array([1, 5, 2]).astype('float32')
            }

        x = fluid.data(name="x", shape=[3], dtype='float32')
        y = fluid.data(name="y", shape=[3], dtype='float32')
        z = paddle.div(x, y)
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])

        print(z_value) # [2., 0.6, 2.]


    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.ones((2, 3, 4, 5)).astype('float32'),
                "y": np.zeros((4, 5)).astype('float32')
            }

        x = fluid.data(name="x", shape=[2, 3, 4, 5], dtype='float32')
        y = fluid.data(name="y", shape=[4, 5], dtype='float32')
        z = paddle.div(x, y, name='z')
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])

        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
                "y": np.random.randint(1, 5, size=[5]).astype('float32')
            }

        x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
        y = fluid.data(name="y", shape=[5], dtype='float32')
        output = fluid.data(name="output", shape=[2,3,4,5], dtype="float32")
        z = paddle.div(x, y, out=output)
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])
        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        with fluid.dygraph.guard(fluid.CPUPlace()):
            np_x = np.array([2, 3, 4]).astype('float64')
            np_y = np.array([1, 5, 2]).astype('float64')
            x = fluid.dygraph.to_variable(np_x)
            y = fluid.dygraph.to_variable(np_y)
            z = paddle.div(x, y)
            np_z = z.numpy()
            print(np_z)  # [2., 0.6, 2.]

    """
    op_type = 'elementwise_div'
    axis = -1
    act = None
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)

    original_op_type = 'div'
    if name and out:
        warnings.warn(
            "Both name and out parameters have been set in paddle.tensor.%s, only out will take effect to specify the result storage. "
            "You can discard either one to solve this warning." %
            original_op_type,
            category=UserWarning,
            stacklevel=2)
    return _elementwise_op(LayerHelper(op_type, **locals()))


for func in [
        add,
        div,
]:
    proto_dict = {'add': 'elementwise_add', 'div': 'elementwise_div'}
    op_proto = OpProtoHolder.instance().get_op_proto(proto_dict[func.__name__])
    if func.__name__ in ['add']:
        additional_args_lines = [
            "alpha (int|float, optional): The alpha factor of the input. Default is 1. If alpha is not 1, the equation becomes Out = X + alpha * Y.",
            "out (Variable, optinal): The Variable that stores results of the operation. Default is None. If out is None, \
            a new Variable will be created to store the results."
                                                                 ,
            "name (string, optional): Name of the output. \
            Default is None. It's used to print debug info for developers. Details: \
            :ref:`api_guide_Name` "
        ]
    else:
        additional_args_lines = [
            "out (Variable, optinal): The Variable that stores results of the operation. If out is None, \
            a new Variable will be created to store the results."
                                                                 ,
            "name (string, optional): Name of the output. \
            Default is None. It's used to print debug info for developers. Details: \
            :ref:`api_guide_Name` "
        ]

    func.__doc__ = _generate_doc_string_(
        op_proto,
        additional_args_lines=additional_args_lines,
        skip_attrs_set={"x_data_format", "y_data_format", "axis"
                        }) + """\n""" + str(func.__doc__)

def sum(input, dim=None, dtype=None, keep_dim=False, name=None):
    """
    Computes the sum of tensor elements over the given dimension.

    Args:
        input (Variable): The input variable which is a Tensor, the data type is float32,
            float64, int32, int64.
        dim (list|int, optional): The dimensions along which the sum is performed. If
            :attr:`None`, sum all elements of :attr:`input` and return a
            Tensor variable with a single element, otherwise must be in the
            range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
            the dimension to reduce is :math:`rank + dim[i]`.
        dtype(str, optional): The dtype of output tensor. The default value is None, the dtype 
            of output is the same as input tensor.
        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
            than the :attr:`input` unless :attr:`keep_dim` is true, default
            value is False.
        name(str, optional): The default value is None.  Normally there is no need for
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`

    Returns:
        Variable: Tensor, results of summation operation on the specified dim of input tensor,
        it's data type is the same as input's Tensor.

    Raises:
        ValueError, the :attr:`dtype` must be float64 or int64.
    
    Examples:
        .. code-block:: python

            import paddle
            import paddle.fluid as fluid
            # x is a Tensor variable with following elements:
            #    [[0.2, 0.3, 0.5, 0.9]
            #     [0.1, 0.2, 0.6, 0.7]]
            # Each example is followed by the corresponding output tensor.
            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
            out1 = paddle.sum(x)  # [3.5]
            out2 = paddle.sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
            out3 = paddle.sum(x, dim=-1)  # [1.9, 1.6]
            out4 = paddle.sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]

            # y is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1, 2], [3, 4]],
            #      [[5, 6], [7, 8]]]
            # Each example is followed by the corresponding output tensor.
            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
            out5 = paddle.sum(y, dim=[1, 2]) # [10, 26]
            out6 = paddle.sum(y, dim=[0, 1]) # [16, 20]

    """
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
    attrs = {
        'dim': dim if dim != None and dim != [] else [0],
        'keep_dim': keep_dim,
        'reduce_all': True if dim == None or dim == [] else False,
    }
    dtype_flag = False
    if dtype is not None:
        if dtype in ['float64', 'int64']:
            if (convert_dtype(input.dtype) == "float32" and dtype == "float64") or \
               (convert_dtype(input.dtype) == "int32" and dtype == "int64"):
                attrs.update({
                    'in_dtype': input.dtype,
                    'out_dtype': convert_np_dtype_to_dtype_(dtype)
                })
                dtype_flag = True
        else:
            raise ValueError(
                "The value of 'dtype' in sum op must be float64, int64, but received of {}".
                format(dtype))

    if in_dygraph_mode():
        reduce_all = True if dim == None or dim == [] else False
        dim = dim if dim != None and dim != [] else [0]
        if dtype_flag:
            return core.ops.reduce_sum(input, 'dim', dim, 'keep_dim', keep_dim,
                                       'reduce_all', reduce_all, 'in_dtype',
                                       input.dtype, 'out_dtype',
                                       convert_np_dtype_to_dtype_(dtype))
        else:
            return core.ops.reduce_sum(input, 'dim', dim, 'keep_dim', keep_dim,
                                       'reduce_all', reduce_all)
    check_variable_and_dtype(
        input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_sum')
    helper = LayerHelper('sum', **locals())
    if dtype_flag:
        out = helper.create_variable_for_type_inference(
            dtype=convert_np_dtype_to_dtype_(dtype))
    else:
        out = helper.create_variable_for_type_inference(dtype=input.dtype)
    helper.append_op(
        type='reduce_sum',
        inputs={'X': input},
        outputs={'Out': out},
        attrs=attrs)
    return out